KEYWORDS: Digital watermarking, Singular value decomposition, Medical imaging, Image processing, Lung, COVID 19, X-ray imaging, Optical information security
This work presents a watermarking algorithm applied to medical images by using the Steered Hermite Transform (SHT), the Singular Value Decomposition (SVD), and the Jigsaw transform (JS). The principal objective is to protect the patient’s information using imperceptible watermarking and preserve its diagnosis. Thus, the watermark imperceptibility is achieved using the high-order Steered Hermite coefficients, whereas the SVD decomposition and the JS ensure the watermark against attacks. We use the medicine symbol Caduceus as a watermark. The metrics employed to evaluate the algorithm’s performance are the Peak Signal-to-Noise Ratio (PSNR), the Mean Structural Similarity Index (MSSIM), and the Normalized Cross-Correlation (NCC). The evaluation metrics over the watermarked image show that it does not suffer quantitative and qualitative changes, and the extracted watermark was recovered successfully with high PSNR values. In addition, several watermark extraction tests were performed against geometric and common processing attacks. These tests show that the proposed algorithm is robust under critical conditions of attacks, for example, against nonlinear smoothing (median filter), high noise addition (Gaussian and Salt & Pepper noise), high compression rates (JPEG compression), rotation between 0 to 180 degree, and translations up to 100 pixels.
Nowadays eye diseases that are not treated in a timely manner can lead to blindness in the patient. Diabetic retinopathy and retinopathy of prematurity are a couple of conditions considered to be the main causes of blindness in both adults and children. The technique used to date to verify the status of the retina is a qualitative analysis by an ophthalmological expert of fundus images. However, this is entirely based on the experience acquired by the physician and being able to detect changes in the vascular structure of the retina is a great challenge which can be addressed through technology. This paper presents a novel method to carry out the numerical modeling of the major temporal arcade using orthogonal polynomials of Legendre, Chebyshev and Laguerre through a genetic algorithm that helps to determine the coefficients of the linear combination of each one. A set of twenty fundus images already outlined by an expert was used, which were processed by the algorithm, generating an adjustment curve on the set of pixels of the Major Temporal Arcade. The results obtained were compared with three existing methodologies in the literature by using two metrics, emerging the Legendre polynomials as the most suitable for modeling, as a consequence of the low values obtained in the metrics compared to the other methods.
In this paper, we present a new non-contact strategy to estimate the Peripheral Oxygen Saturation (SPO2) based on the Eulerian motion video magnification technique and a signal processing technique, The magnification procedure was carried out using two approaches : the Hermite decomposition and the Gaussian decomposition. The SpO2 is estimated from the signals extracted after magnification process using the red and the blue Chanel of the frame. We have tested the method on five healthy subjects using videos obtained from the google-meet video conference platform. To compare the performance of the methods, we compute the mean average error and metrics issues from the Bland and Altman analysis to investigate the agreement of the methods with respect to a contact pulse oximeter device as reference. The proposed solution shows an agreement with respect to the reference of most of 98%
KEYWORDS: Cryptography, Image encryption, Medical imaging, Information visualization, Visualization, RGB color model, Image processing, Statistical analysis, Medical diagnostics
In this work, we propose a novel medical image encryption algorithm. It is based on the Jigsaw transform, Langton’s ant, cyclic permutation, and a novelty way to add deterministic noise. The robustness of our algorithm has been proven through several testing, such as statistical analysis (histograms and correlation distributions), visual testing, entropy testing and key space assessment, showing in each one a high-security level.
This work presents a watermarking algorithm applied to medical images of COVID-19 patients, intending to preserve its diagnose and that it not be modified when watermark will be inserted. Besides, we tried to protect the information of the patient using an imperceptible watermarking. Our technique is based on a perceptive approach to insert the watermark by decomposing the medical image using the Hermite transform. We use as watermark two image logos, including text strings to demonstrate that the watermark can contain relevant information of the patient. Some metrics were applied to evaluate the performance of the algorithm. Finally, we present some results about robustness with some attacks applied to watermark images.
In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and an Artificial Hydrocarbon Networks (AHN) as classifier. After the magnification procedure, a AHN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. The respiratory rate (RR) is estimated from the classified frames. We have tested the method on 10 healthy subjects in different positions. To compare performance of methods to respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for our strategy is 4.46 ± 3.68% with and agreement with respect of the reference of ≈ 98%.
Age-related macular degeneration (AMD) is the leading cause of irreversible blindness in industrialized countries. It is estimated that AMD affects at least 1 in 10 Hispanics. Previous reports have shown that AMD has multiple risk factors. Recently, we demonstrated that some genetic variants in the SGCD gene are involved in AMD developments, especially in early-stage (geographic atrophy, GA). Therefore, to evaluate the relationship between SGCD's absence and the loss of photoreceptors in GA, we worked with a genetically modified mouse model, SGCD deficient (Sgcd−/−) and a control mouse C57BL/6J (Sgcd+/+). First, we obtained hematoxylin and eosin (H&E) retina staining microscopic images. Then, we coarsely selected the outer and inner nuclear retinal layer (ONL and INL respectively) and finally, we applied an automatic nuclei segmentation to calculate the nuclear density in each region. Our results showed that Sgcd absence does not result in photoreceptor loss, on the contrary, it promotes an increment in nuclear density by 8.7% in ONL and 20.1% in INL compared with control eyes (p = 0.0033 and p < 0.0001 respectively). This could be explained by the fact that SGCD codifies the delta-sarcoglycan protein and there is evidence that showed a relationship between the absence of this protein with the activation of a cell proliferation signaling pathway. Finally, our results show that the delta-sarcoglycan protein could play an important role in the pathogenesis of the geographic atrophy. Moreover, there are promising perspectives for the systematic approach applied for cell image analysis, as an important tool to determine the nuclear density for assessing the progression of AMD.
In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and a system based on a Convolutional Neural Network (CNN). After the magnification procedure, a CNN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. Two strategies are used as input to the CNN. A CNN-ROI proposal where a region of interest is selected manually on the image frame and in the second case, a CNN-Whole-Image proposal where the entire image frame is selected. Finally, the RR is estimated from the classified frames. The CNN-ROI proposal is tested on five subjects in lying face down position and it is compared to a procedure using different image processing steps to tag the frames as inhalation or exhalation. The mean average error in percentage obtained for this proposal is 2.326±1.144%. The CNN-whole-image proposal is tested on eight subjects in lying face down position. The mean average error in percentage obtained for this proposal is 2.115 ± 1.135%.
This study proposes a new tool based on Virtual Reality (VR) as a complement in the treatment of people diagnosed with Autism Spectrum Disorder (ASD). VR tools have been stablished in last years as a new option in learning and practising new skills during the treatment. In this work, a VR application is developed simulating several environments corresponding to different types of emotions according to the Gestalt school of psychology. The VR application was tested in five male teenagers diagnosed with ASD of level one according to the DSM-5 during the therapy sessions. A qualitative evaluation of the VR application is carried out by the therapist during the session. It is observed and annotated which emotions have been detonated by the VR application giving to the therapist new information for the subsequent sessions.
In this work we present a combination of segmentation and motion estimation methods applied to left ventricle evaluation in fetal echocardiographic images which are used for prenatal diagnosis. In our proposed scheme, several features of the ultrasound images are computed and used for both algorithms. A multiresolution framework is employed for the segmentation and motion estimation tasks. The segmentation is achieved using a multi-texture active appearance model based on the Hermite transform. The analysis is performed using the appearance models provided by Hermite coefficients up to third order. The multiresolution approach allows to obtain a robust segmentation to extract the shape of the left ventricle. The obtained results in the segmentation step are used for the motion estimation algorithm. The left ventricle is the structure used for evaluation. The main goal is to determinate the heart movement of fetal heart which can be used for disease detection, characterization and further analysis. Results of the motion estimation process are analyzed and compared with other techniques applied to heart ultrasound data.
In this paper we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion magnification technique and a system based on different images processing steps. After the magnification procedure, a ROI is selected manually, an enhancement algorithm based on an adaptive histogram equalization is applied and finally the frames are binarized using the Otsu algorithm. Morphological operations are carry out on the video frames and a tracking temporal strategy is implemented to estimate the breathing rate. The magnification procedure was carried out using an Hermite decomposition. We have tested the method on three subjects in four positions (seat, lying face down, lying face up and lying in fetal position). The motion magnification approach is compared to the Laplacian decomposition strategy computing the mean absolute error.
In this paper we present a new Eulerian phase-based motion magnification technique using the Hermite Transform (HT) decomposition that is inspired in the Human Vision System (HVS). We test our method in one sequence of the breathing of a newborn baby and on a video sequence that shows the heartbeat on the wrist. We detect and magnify the heart pulse applying our technique. Our motion magnification approach is compared to the Laplacian phase based approach by means of quantitative metrics (based on the RMS error and the Fourier transform) to measure the quality of both reconstruction and magnification. In addition a noise robustness analysis is performed for the two methods.
In this paper we present an Eulerian motion magnification technique using a spatial decomposition based on the Steered Hermite Transform (SHT) which is inspired in the Human Vision System (HVS). We test our method in one sequence of the breathing of a newborn baby and on a video sequence that shows the heartbeat on the wrist. We estimate the heart pulse applying the Fourier transform on the magnified sequences. Our motion magnification approach is compared to the Laplacian and the Cartesian Hermite decomposition strategies by means of quantitative metrics.
Heart diseases are one of the most important causes of death in the Western world. It is, then, important to implement algorithms to aid the specialist in analyzing the heart motion. We propose a new strategy to estimate the cardiac motion through a 3D optical flow differential technique that uses the Steered Hermite transform (SHT). SHT is a tool that performs a decomposition of the images in a base that model the visual patterns used by the human vision system (HSV) for processing the information. The 3D + t analysis allows to describe most of motions of the heart, for example, the twisting motion that takes place on every beat cycle and to identify abnormalities of the heart walls. Our proposal was tested on two phantoms and on two sequences of cardiac CT images corresponding to two different patients. We evaluate our method using a reconstruction schema, for this, the resulting 3D optical flow was applied over the volume at time t to obtain a estimated volume at time t + 1. We compared our 3D optical flow approach to the classical Horn and Shunk's 3D algorithm for different levels of noise.
We present an Eulerian motion magnification technique with a spatial decomposition based on the Hermite
Transform (HT). We compare our results to the approach presented in.1 We test our method in one sequence of
the breathing of a newborn baby and on an MRI left ventricle sequence. Methods are compared using quantitative
and qualitative metrics after the application of the motion magnification algorithm.
Medical image watermarking is an open area for research and is a solution for the protection of copyright and intellectual property. One of the main challenges of this problem is that the marked images should not differ perceptually from the original images allowing a correct diagnosis and authentication. Furthermore, we also aim at obtaining watermarked images with very little numerical distortion so that computer vision tasks such as segmentation of important anatomical structures do not be impaired or affected. We propose a preliminary watermarking application in cardiac CT images based on a perceptive approach that includes a brightness model to generate a perceptive mask and identify the image regions where the watermark detection becomes a difficult task for the human eye. We propose a normalization scheme of the image in order to improve robustness against geometric attacks. We follow a spread spectrum technique to insert an alphanumeric code, such as patient’s information, within the watermark. The watermark scheme is based on the Hermite transform as a bio-inspired image representation model. In order to evaluate the numerical integrity of the image data after watermarking, we perform a segmentation task based on deformable models. The segmentation technique is based on a vector-value level sets method such that, given a curve in a specific image, and subject to some constraints, the curve can evolve in order to detect objects. In order to stimulate the curve evolution we introduce simultaneously some image features like the gray level and the steered Hermite coefficients as texture descriptors. Segmentation performance was assessed by means of the Dice index and the Hausdorff distance. We tested different mark sizes and different insertion schemes on images that were later segmented either automatic or manual by physicians.
The left ventricle (LV) segmentation plays an important role in a subsequent process for the functional analysis of the LV. Typical segmentation of the endocardium wall in the ventricle excludes papillary muscles which leads to an incorrect measure of the ejected volume in the LV. In this paper we present a new variational strategy using a 2D level set framework that includes a local term for enhancing the low contrast structures and a 2D shape model. The shape model in the level set method is propagated to all image sequences corresponding to the cardiac cycles through the optical flow approach using the Hermite transform. To evaluate our strategy we use the Dice index and the Hausdorff distance to compare the segmentation results with the manual segmentation carried out by the physician.
Medical image analysis has become an important tool for improving medical diagnosis and planning treatments. It involves volume or still image segmentation that plays a critical role in understanding image content by facilitating extraction of the anatomical organ or region-of-interest. It also may help towards the construction of reliable computer-aided diagnosis systems. Specifically, level set methods have emerged as a general framework for image segmentation; such methods are mainly based on gradient information and provide satisfactory results. However, the noise inherent to images and the lack of contrast information between adjacent regions hamper the performance of the algorithms, thus, others proposals have been suggested in the literature. For instance, characterization of regions as statistical parametric models to handle level set evolution. In this paper, we study the influence of texture on a level-set-based segmentation and propose the use of Hermite features that are incorporated into the level set model to improve organ segmentation that may be useful for quantifying left ventricular blood flow. The proposal was also compared against other texture descriptors such as local binary patterns, Image derivatives, and Hounsfield low attenuation values.
This article describes a perceptual approach to calculate the optical flow estimation of the left ventricle in a short axis view of the heart in computer tomography images. The method is based on the the Hermite transform which is an image representation model that incorporates some of the more important properties of the first stages of the human visual system. Our optical flow estimation approach incorporates a differential approach that uses the steered Hermite coefficients as local constraints and uses the implicit multiresolution scheme of the Hermite transform to compute large displacements. It also involves several of the constraints seen in the current differential methods which allows obtaining an accurate optical flow. We use the anatomic short axis view of the heart to calculate the optical flow estimation instead of the original CT images of the axial plane. This view allows visualizing the left ventricle like a circular structure, which is more suitable for visualization of the left ventricle motion.
Considering the importance of studying the movement of certain cardiac structures such as left ventricle and myocardial wall for better medical diagnosis, we propose a method for motion estimation and image segmentation in sequential Computed Tomography images. Two main tasks are tackled. The first one consists of a method to estimate the heart's motion based on a bio-inspired image representation model. Our proposal for optical flow estimation incorporates image structure information extracted from the steered Hermite transform coefficients that is later used as local motion constraints in a differential estimation approach. The second task deals with cardiac structure segmentation in time series of cardiac images based on deformable models. The goal is to extend active shape models (ASM) of 2D objects to the problem of 3D (2D + time) cardiac CT image modeling. The segmentation is achieved by constructing a point distribution model (PDM) that encodes the spatio-temporal variability of a training set. Combination of both motion estimation and image segmentation allows isolating motion in cardiac structures of medical interest such as ventricle walls.
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